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AI/ML Textbook
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  • Textbook Content
  • Part 1 Mathematical Foundations for Machine Learning
    • Ch 1: Linear Algebra for ML
    • Ch 2: Calculus and Optimization
    • Ch 3: Probability and Statistics
  • Part 2 Python Ecosystem and Classical Machine Learning
    • Ch 4: Python Data Science Stack
    • Ch 5: Supervised Learning Fundamentals
    • Ch 6: Classification Algorithms
    • Ch 7: Ensemble Methods
    • Ch 8: Unsupervised Learning
  • Part 3 Deep Learning Fundamentals
    • Ch 9: Neural Network Foundations
    • Ch 10: Training Deep Networks
    • Ch 11: Convolutional Neural Networks
    • Ch 12: Sequence Models
  • Part 4 Modern Architectures and Large Language Models
    • Ch 13: Transformer Architecture
    • Ch 14: Pre-trained Language Models
    • Ch 15: Fine-tuning and Alignment
    • Ch 16: Efficient Transformer Architectures
    • Ch 17: Vision Transformers
  • Part 5 Generative Models
    • Ch 18: Variational Autoencoders
    • Ch 19: Generative Adversarial Networks
    • Ch 20: Diffusion Models
  • Part 6 Multimodal AI and Vision-Language Models
    • Ch 21: Vision-Language Foundations
    • Ch 22: Text-to-Image and Beyond
  • Part 7 Applied AI Systems
    • Ch 23: Retrieval-Augmented Generation
    • Ch 24: Agentic AI Systems
    • Ch 25: Reinforcement Learning
    • Ch 26: MLOps and Production Systems
    • Ch 27: AI Ethics and Responsible Development
  • Appendices
  • Appendix A: Environment Setup Guide
  • Appendix B: GPU Computing Fundamentals
  • Appendix C: Cloud Platform Quick Start
  • Appendix D: Mathematics Reference
  • Appendix E: Library Quick Reference
  • Resources
  • Library Reference
  • Learning Paths
Home / Part 7

Part 7: Applied AI Systems

Build production-ready AI systems including RAG, agents, reinforcement learning, and MLOps pipelines.


Chapters in This Part

Chapter 23: Retrieval-Augmented Generation

RAG fundamentals, vector databases, embedding models, and advanced patterns....

Intermediate Expert
150 min
Chapter 24: Agentic AI Systems

Agent architecture, tool use, multi-agent systems, and safety....

Advanced Expert
150 min
Chapter 25: Reinforcement Learning

RL fundamentals, policy gradients, PPO, and RLHF....

Intermediate Expert
120 min
Chapter 26: MLOps and Production Systems

Experiment tracking, deployment, monitoring, and scaling....

Intermediate Advanced
120 min
Chapter 27: AI Ethics and Responsible Development

Bias, fairness, interpretability, privacy, and governance....

Intermediate
90 min
Part 6: Multimodal AI and Vision-Language Models

© 2026 AI/ML Engineering Textbook

A comprehensive guide to AI/ML engineering, from mathematical foundations to agentic systems.